US7373669B2 - Method and system for determining presence of probable error or fraud in a data set by linking common data values or elements - Google Patents
Method and system for determining presence of probable error or fraud in a data set by linking common data values or elements Download PDFInfo
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- US7373669B2 US7373669B2 US10/640,826 US64082603A US7373669B2 US 7373669 B2 US7373669 B2 US 7373669B2 US 64082603 A US64082603 A US 64082603A US 7373669 B2 US7373669 B2 US 7373669B2
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- 238000000034 method Methods 0.000 title claims abstract description 45
- GNFTZDOKVXKIBK-UHFFFAOYSA-N 3-(2-methoxyethoxy)benzohydrazide Chemical compound COCCOC1=CC=CC(C(=O)NN)=C1 GNFTZDOKVXKIBK-UHFFFAOYSA-N 0.000 claims 2
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- 238000004458 analytical method Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/02—Banking, e.g. interest calculation or account maintenance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y10S—TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y10S707/00—Data processing: database and file management or data structures
- Y10S707/99931—Database or file accessing
- Y10S707/99933—Query processing, i.e. searching
Definitions
- This invention relates to data analysis and more specifically to the analysis of data for possible or probable fraud or error and for correspondingly minimizing the liability and exposure of individuals and/or entities as a consequence of compromised credit card information.
- a single data field taken from a suspected fraudulent transaction or entry is used to track all related transactions or entries and in the case of credit card transactions used to minimize liability to the credit card company and the proper credit card holder.
- the invention can detect additional fraudulent or “hidden” entities which may have used a common (and probably compromised) data key value and/or element to the originally suspected fraudulent or error-containing transaction or entry. This helps to search for transactions, identities and information that otherwise wish to remain hidden.
- the present method can help with maintaining financial records, eliminating or reducing money laundering, tracking fraud and/or terrorism and preventing the use of compromised information in the same or other databases by those who otherwise wish to conceal their improper transactions, entries and/or identities.
- the project currently in its nascent stages, depends or will depend at least in part on analyzing data, especially transactional and credit card transactional data, to discover patterns of activity that will lead the authorities to the whereabouts and identities (and possibly their plans and objects) of terrorists and their organizations.
- the present invention is also applicable to matters of lesser importance than national security, e.g., determining if a stolen credit card has been used or its number compromised and unauthorizedly used one or more times by another or many others. These thieves often try to establish new transactions with the same credit card number, and/or identity.
- Identity theft is today a serious problem.
- the present invention seeks to minimize the use of key information obtained by one who improperly gains access to a credit card transaction and uses the information, not just the credit card number, to his unlawful advantage.
- Transactional data can be analyzed, one transaction at a time for indications of possible fraud or error.
- fraud occurs when incorrect information is deliberately included in a credit card-type or identify verifying-like transaction, typically to deceive the recipient of the data into releasing goods, services or information.
- the most common example is when a stolen card or number is used to buy goods over the internet (or telephone) with the goods being sent to a different address than that normally associated with the credit card holder.
- Error occurs when incorrect information is inadvertently included in the transactional record.
- a single credit card transaction over the phone or internet can include a name, a billing address (including street, city, state, zip code, country, etc.), an e-mail address, a credit card number, a shipping address; and an expiration date for the credit card.
- the actual residence or business location for billing purposes corresponding to the zip code of the shipping address can be compared.
- the business address (number and street, along with town or city) can also be compared to the actual city and state corresponding to the given zip code. If there is a discrepancy, an error or fraud can be suspected. If the given zip code in the transaction corresponds to the actual zip code of the information on a database, then there is a greater possibility that the transaction is legitimate.
- IP Internet Protocol
- the host name or number of the Internet Protocol (“IP”) source address can be used with a reverse IP lookup utility database to determine if the recorded host name in the database is associated with the now given source address. This, too, can be useful in detecting error or possible fraud.
- Information corresponding to the automatically transmitted host name can also be compared with information manually entered in the online transaction. For example, if the host is located in Weg and the address submitted in the transaction is Japan, then the transaction seems suspect.
- This, according to the present invention, is basically accomplished by connecting all of “the dots” by using information in the database in a thorough and scientific way (vs. random).
- the invention is a method for detecting fraudulent or erroneous data from a transaction data set.
- a transaction record thought to have been compromised and having a plurality of key values is selected from a transaction record database.
- One of the key values is selected from the selected transaction record.
- the entire transaction record database is then queried for transaction records having the same selected key value.
- a first record of a database could be considered, even if no fraud is suspected.
- a first key value taken from it is used as the queried entry for other records to see if it is linked to other transactions or records. All selected key values of the first record are searched through the database. Uncovered records are added to a list. Then, the key values of those records used to search the database (avoiding duplicative queries).
- a listing of possible erroneous or fraudulent transactions or records is developed.
- each data or key value element can become a thread from which the entire rug of possible deceit may be woven—no matter which data element or key value you begin with of a record, you will find all related records.
- a second or subset of transactions of the database is thus compiled of transaction records that contain duplicates of the selected key value(s). So, for example, if the e-mail address of a user appears in two records, those records are put into a possible suspect directory or listing.
- the records may be legitimate. However, if the names of the identities or addresses of the users in the records having the same e-mail addresses are different or their actual residences, the use of the same e-mail tends to indicate probable fraud. Looking then for additional transactions where the same e-mail address is used, or the same home residence, may uncover additional fraudulent transactions. At least one other key value is also selected from the original transaction record, and the transaction record database is queried for all transaction records having the second key value.
- the results of the second and any subsequent queries are added to the second database, i.e., the possible suspect directory or listing.
- the main database record is considered to be queried for a key value
- that query is first checked to the list of queries so that the same query will not be repeated on the database. If already done, it is not repeated. If not done, it is added to the query list and done.
- This is economic, efficient, and eliminates possible endless processing loops by the compiler.
- a list of queried key values is compiled into a key value database so as to prevent the same query from being used more than once. Risk coefficients, corresponding to probabilities of error or fraud, are assigned to the results of the queries as a listing of the transaction records satisfying the queries is compiled in the second or suspected transaction listing is created.
- a new query is then made of the transaction record database with either a new key value from the same transaction record initially selected and/or with a new key value from a different record in the transaction record database.
- risk coefficients are assigned to the transaction records in the second database based on the type, quality, or number of queried (or flagged) and common key values a given transaction record has and/or based on the number of transaction records in the second database having a given matching key value.
- the key values of the transaction records in the transaction record database for credit card-type transactions correspond to identification data, including but not limited to a credit card number, a bank account number, a name, an address, an e-mail address, a telephone number, a fax number, a social security number, a merchant identification number, and a product identification number.
- identification data including but not limited to a credit card number, a bank account number, a name, an address, an e-mail address, a telephone number, a fax number, a social security number, a merchant identification number, and a product identification number.
- patterns of purchasing or transactions in general, i.e., the method and system can be used with reference to applications for passports, for a visa, for a driver's license, for loan applications to a financial institution, etc.
- the method and system of the present invention can be used with multiple databases to determine possible error and fraud.
- the databases can be widely varied, as briefly just referred to, and more, of course. For the purposes of the
- the present invention can advantageously detect the risk of fraud or error in a record of a credit card transaction by comparing selected identification data (often referred to as “key values” of one record to the corresponding “key values” in the other records of other transactions in the database.
- key values of one record
- a “key value” means and includes but is not limited to a credit card number, a name, an address, an e-mail address, a “ship to” address, etc.
- even an internally consistent transaction for example, where the address and zip code are correct, but where the transaction is otherwise fraudulent or erroneous can be detected as a higher risk transaction.
- the method and system work best when a suspected transaction of fraud is the first record to be considered and a key value selected from that transaction but the method and system can also be used on an entire database by using certain (or all) key values from any one or more records and then querying the entire database to determine instances of common usage, overlap, and possible fraud or error.
- the essence of the invention is to link known stolen credit card information or fraud with all potential or actual fraud that can be found through comparing key values, both direct matches and also by use of “like” statements for fuzzy-logic matching.
- a record of each credit card transaction is stored in a database.
- Each record can be a correlated set of data that can correspond to a single credit card type transaction whether conducted online, by telephone, in person or in another credit card transaction.
- the record contains two or more fields of information, also known as “keys.”
- each record for an online type of credit card transaction can have the keys “unique record identifier;” “name of credit card holder;” “street address;” “city;” “country;” “state;” “e-mail address;” and “credit card number.”
- the key in each of the fields of a credit card transaction record has a value, whether arithmetic, letters, symbols, or a combination of the same.
- the “unique record identifier” key in one record may have the value “B000139;” the “name of credit card holder” key may have the value “Sanford Q. Burns” ; the e-mail address may have the value “Sburns@LLBL.com,” etc.
- a “data set” can be compiled of all records taken from the master database having certain of the same key values or by using fuzzy logic, related or close to identical key values. The records in the master or transaction database can be reviewed and compared by using a database query language, such as SQL.
- one record in the case of credit card transactions, a suspected compromised credit card number
- a key value for example, the credit card number or the e-mail address
- the initial record selected from the database or set can be one that is known or suspected to be fraudulent or erroneous.
- a record having a credit card key value equal to a credit card number that has been reported to have been stolen can be a good candidate for the starting record.
- the key value that can be selected from this record can be the credit card number key value or the e-mail address of the user (or the user's name or identity).
- the transaction database is then searched for all records having the same key value as the selected key value, i.e., the compromised or stolen credit card number or the e-mail address.
- the transaction database or set is searched for all records having the same stolen credit card number or e-mail address.
- the new records so identified are copied to a second data set called the DataDistinct Data Set.
- a Query Listing showing each of the selected key values which are used for the search of the entire database is recorded and updated each time that the database is reviewed with a new “key value.” Then, at least one other key value from the initial record (or from one of the revealed transactions which used the same credit card number) can be selected, and the transaction data set searched for all records having identical key values to the second selected key value.
- the initial fraudulent record in addition to the stolen credit card number, includes an e-mail address, e.g., “ersatz64@earth.com,” then all of the other records in the transaction database are searched for the value corresponding to the same e-mail address or second key value used in the transaction record having the compromised credit card number. If a second record is found with the same credit card number which was reported as stolen, then the key value, for example, of the e-mail address of that transaction, may be used to detect other transactions with the same e-mail address, even though they may not have used the same credit card number. This transaction, too, may be suspect. The entire database is searched for that key value, the list of Query Listings of key values updated, etc.
- the underlying assumption is that an entity submitting fraudulent transaction information may reuse at least part of the same information from transaction to transaction.
- the key values of the found records can be used as the basis for still further searching. In this way, a cluster of related fraudulent transactions can be uncovered for further investigation.
- the mechanism for formulating and tracking the queries can include placing each query into a list of Query Listings or a data set called SQLCodes.
- SQLCodes the query itself can be the primary key, so that if any duplicate query (a query identical to one already asked) is generated, it cannot be added to SQLCodes, and the query is advantageously not rerun.
- queries can be generated much faster than new records are added to the DataDistinct Data Set. So new queries are queued in the SQLCodes data set, and can be run sequentially, or in any other suitable order.
- Record 3 is identified as a likely fraudulent transaction, for example, because credit card number 78989 has been reported as stolen. Pursuant to the method of the present invention, Record 3 is selected for use of its Key Values and application of those key values to the remainder of the database to determine whether other transactions, even if they have used other credit card numbers, are likely fraudulent:
- a query is first generated to select all records from the transaction database or set (consisting, in the example, of thousands of Transaction Identifiers 1 -x; although number 3 need not be considered in the next step) that have the same or a similar or fuzzy logic-related key value as a selected key value Record 3 .
- the Query ID# field is the query identifier
- “Ran” is an indicator or “flag” of instructions to the user or processor that indicates if the query has yet been run against the balance of the transaction database or set.
- the system is designed to run each of the SQLCodes Data Set queries until all Query ID#'s are reflected as “Yes” under the Ran Query.
- the default value of Ran can and should be “No.”
- the indicator or Ran flag is set to “Yes.”
- a Ran inquiry results in a NULL or empty set, the system or method is complete.
- each “No” indicator directs the system to run that query on the Transaction Data Base.
- the SQLCodes Data Set or Query Listing field can be initially set at the primary key value, i.e. the credit card number which is thought to have been stolen or compromised.
- the primary key e.g., the credit card number or the e-mail address
- Query ID # 1 can be actually run against the entire transaction data set, and all records containing the e-mail address “joe@aol.com” are selected and copied to the DataDistinct Data Set (which already contains the initial transaction under scrutiny). In the example, this will result in the DataDistinct Data Set looking as follows:
- the Transaction Identifier is the identifier for a record in the original Data Base or Set. It can be set, alternatively, as a new number for the Data Distinct Data Set. Meanwhile, the Run flag for Query 1 in the SQLCodes Data Set or Query Listing is changed to “Yes.” This will prevent needlessly rerunning that query.
- Query 2 relating to the credit card number, can then be run, which copies two additional records (Records 164 and 328 ) to the DataDistinct data set:
- This procedure can generate duplicate potential queries.
- the present invention advantageously prevents repeat searches based upon such duplications by the use of the SQLCodes Data Set.
- the query is designated to be a primary key. So any query that is a duplicate of a query already in the SQLCodes Data Set cannot be added and will not be re-run. Since the queries are run from the SQLCodes Data Set, the duplicate query will not be run. This elegant solution prevents repeat searches of the Transaction Data Set for the same records using the same keys or values.
- duplicate records may or may not be added to the DataDistinct Data Set.
- Record 4 will be selected a first time by Query 1 because Record 4 has a key value where the e-mail address joe@aol.com is present.
- Duplicate records can be excluded from the DataDistinct Data Set by making the DataDistinct Data Set record identifier the same as the transaction data set record identifier, and then making the DataDistinct Data Set and only adding to the Data Distinct Data Set when a new Transaction Identifier is located and found.
- the records that have been determined to be directly or indirectly related to a bad record can, if desired, be copied into a Suspect Data Set, and can be assigned an indicator or value as to the risk associated with each record, group of records, or the whole Suspect Data Set considered highly suspect.
- a record can be placed into a High Risk Data Set, a Medium Risk Data Set, or a Low Risk Data Set (types of Suspect Data Sets), depending upon the risk level associated with the record and the common use of the key values and their characteristics.
- the Suspect Data Set may include:
- the indications of risk level can be made in accordance with any suitable rule.
- a record that an e-mail address with a different name and credit card number may be considered a risk level of “High.”
- a record that shares the credit card key value with no more than one other key value of a record is accorded a risk level of “Medium” since many family members may share a credit card but have different e-mail addresses and names.
- Transactions can be treated according to their risk level. For example, high risk transactions can be automatically rejected. Medium risk transactions can be referred to a human researcher for further investigation. Low risk transactions can be automatically accepted and processed.
- a key value can be used as the basis for searching for similar key values in other records in the transaction data base or set, not only identical records.
- the keys can be searched in multiple databases, such as looking for people with the claimed same SSN, Driver License numbers and Passports and all their combinations.
- the uncovering of 78989 as the value for several credit card numbers in a few transactions may cause the method to run a Query Listing where e-mail name is “alphonse@nrta.edu” or wilma@stone.net. If the DataDistinct Data Set results in additional transactions which use the same CCN, the same e-mail name values, but different name values, these transactions, too, can be added to the Suspect Data Set. As transactions and key values are added to the Data Distinct Data Set of transactions, the system keeps on looking in the SQLCodes for new key values.
- the system is run with the key value seeking other transactions with the same e-mail address: joe@aol.com. This may or may not uncover other records but if it does, they are added to the DataDistinct Data Set. Then the system looks for the common transactions with CCN of the first transaction and records them, too, unless already indicated on the Data Distinct Data Set. Then, the database is run for the Name key value. After those logic runs are performed, the next transaction recorded in the DataDistinct Data Set is looked at. If the e-mail query is the same as that already done, it is not redone.
- wildcards can be used to detect similar (as well as identical) records. For example, if numerous bad transactions have been detected with e-mail key values that conform to *@whodunnit.com, where “*” is a wildcard, that can represent any string of any length, then a query can be formulated: SELECT Email FROM DataSet WHERE (EMAIL IS LIKE “*@whodunnit.com”).
- This query can advantageously identify all records having e-mail key values with the domain name “whodunnit.com”, such as “al@whodunnit.com” smith@whodunnit.com”, etc.
- any wildcard can be used in formulating such queries. For example, “!” can represent any single character.
- Other Boolean expressions e.g., AND, OR, etc.
Abstract
Description
Transaction Data Base |
Transaction | Credit Card | ||
Identifier | User Name | E-Mail Address | Number |
1 | Alan | al@aol.com | 12345 |
2 | Daniel | dan@earth.com | 45287 |
3 | Joe | joe@aol.com | 78989 |
4 | Phil | joe@aol.com | 12345 |
5 | Carl | carl@sylix.com | 45287 |
. | |||
. | |||
. | |||
164 | Tracey | alphonse@nrta.edu | 78989 |
. | |||
. | |||
. | |||
328 | Wilma | wilma@stone.net | 78989 |
. | |||
. | |||
. | |||
Transaction | |||
Identifier | User Name | E-Mail Address | Credit Card Number |
3 | Joe | joe@aol.com | 78989 |
A query is first generated to select all records from the transaction database or set (consisting, in the example, of thousands of Transaction Identifiers 1-x; although number 3 need not be considered in the next step) that have the same or a similar or fuzzy logic-related key value as a selected key value Record 3. For example, the query “SELECT Email FROM DataSet WHERE (EMAIL=‘Joe@aol.com’)” is generated. It could, of course, be a query for the user's home address, or the IP address used for the online transaction, whatever keys and key values are maintained in the records. In the example, however, for simplicity it is the query for the e-mail name given by the user of the credit card number under consideration in the transaction. This query (for the e-mail address) is then added to an SQLCodes Data Set or Query Listing table:
SQLCodes Data Set |
Query ID# | Query | Ran |
1 | SELECT Email FROM DataSet WHERE | No |
(EMAIL=‘joe@aol.com’) | ||
The Query ID# field is the query identifier, and “Ran” is an indicator or “flag” of instructions to the user or processor that indicates if the query has yet been run against the balance of the transaction database or set. The system is designed to run each of the SQLCodes Data Set queries until all Query ID#'s are reflected as “Yes” under the Ran Query. The default value of Ran can and should be “No.” After the SQL query has been run (i.e., the question has been asked by the computer system of the transaction database or set and the results entered into the DataDistinct Data Set), the indicator or Ran flag is set to “Yes.” When a Ran inquiry results in a NULL or empty set, the system or method is complete. Until then, each “No” indicator directs the system to run that query on the Transaction Data Base. The SQLCodes Data Set or Query Listing field can be initially set at the primary key value, i.e. the credit card number which is thought to have been stolen or compromised. One of the advantages of using the SQLCodes Data Set is that it will enable the investigator to know whether a particular query was entered or not. Other advantages of maintaining a listing of the queried key values is to allow instant access by the investigator as to which query has already been run against the transaction database or set; to know how many queries there are; and how many have been run; and to limit the queries to one type of each (to reduce redundancy). By first setting the Query Listing field to the primary key e.g., the credit card number or the e-mail address, one also is given knowledge of which question is next (e.g., by issuing the command, “Select SQL FROM SQLCodes WHERE (Ran=No”); to know when one is finished asking questions (e.g., receiving a null return value from the query, “Select SQL FROM SQLCodes WHERE (Ran=No”); and to have an audit trail that can show how a “tree” of related records was developed by following the Query Listing or SQLCodes sequence.
SQLCodes Data Set |
Query ID# | Query | Ran |
1 | SELECT Email FROM DataSet WHERE | No |
(EMAIL=‘joe@aol.com’) | ||
2 | SELECT CCN FROM DataSet WHERE | No |
(CCN=‘78989) | ||
Also, the query “SELECT Name FROM DataSet WHERE (NAME=Joe)” can be generated and added to the SQLCodes Data Set or Query Listing:
SQLCodes Data Set |
Query ID# | Query | Ran |
1 | SELECT Email FROM DataSet WHERE | No |
(EMAIL=‘joe@aol.com’) | ||
2 | SELECT CCN FROM DataSet WHERE | No |
(CCN=‘78989) | ||
3 | SELECT Name FROM DataSet WHERE | No |
(NAME=Joe) | ||
DataDistinct Data Set |
Transaction | |||
Identifier | Name | Credit Card Number | |
3 | Joe | joe@aol.com | 78989 |
4 | Phil | joe@aol.com | 12345 |
The Transaction Identifier is the identifier for a record in the original Data Base or Set. It can be set, alternatively, as a new number for the Data Distinct Data Set. Meanwhile, the Run flag for Query 1 in the SQLCodes Data Set or Query Listing is changed to “Yes.” This will prevent needlessly rerunning that query. Query 2, relating to the credit card number, can then be run, which copies two additional records (Records 164 and 328) to the DataDistinct data set:
DataDistinct Data Set |
Transaction | |||
Identifier | Name | Credit Card Number | |
3 | Joe | joe@aol.com | 78989 |
4 | Phil | joe@aol.com | 12345 |
164 | Tracey | alphonse@nrta.edu | 78989 |
328 | Wilma | wilma@stone.net | 78989 |
Records 164 and 328 have no other key values in common with records 3 or 4, except the credit card key value 78989. The discovery of these additional records increases the magnitude or probability of risk associated with all of the selected records (3, 4, 164 and 328), because several apparently unrelated individuals, some with different e-mail addresses do not ordinarily share the same credit card number, unless the number is stolen and it has been compromised. Therefore, each of these transactions is assigned a “risk” of being fraudulent or erroneous, and some corrective or further investigative action can be taken. Such appropriate action can range from further investigation to automatically blocking the fulfillment of the transactions or at least until the investigation is complete.
Suspect Data Set |
Transaction | Credit Card | |||
Identifier | Name | Number | Risk | |
3 | Joe | joe@aol.com | 78989 | High |
4 | Phil | joe@aol.com | 12345 | High |
164 | Tracey | alphonse@nrta.edu | 78989 | Medium |
328 | Wilma | wilma@stone.net | 78989 | Medium |
The indications of risk level can be made in accordance with any suitable rule. In this example, a record that an e-mail address with a different name and credit card number may be considered a risk level of “High.” A record that shares the credit card key value with no more than one other key value of a record is accorded a risk level of “Medium” since many family members may share a credit card but have different e-mail addresses and names. Transactions can be treated according to their risk level. For example, high risk transactions can be automatically rejected. Medium risk transactions can be referred to a human researcher for further investigation. Low risk transactions can be automatically accepted and processed.
Claims (24)
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US10/640,826 US7373669B2 (en) | 2003-08-13 | 2003-08-13 | Method and system for determining presence of probable error or fraud in a data set by linking common data values or elements |
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Cited By (100)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20070069006A1 (en) * | 2005-09-02 | 2007-03-29 | Honda Motor Co., Ltd. | Automated Handling of Exceptions in Financial Transaction Records |
US20070100716A1 (en) * | 2005-09-02 | 2007-05-03 | Honda Motor Co., Ltd. | Financial Transaction Controls Using Sending And Receiving Control Data |
US20070100717A1 (en) * | 2005-09-02 | 2007-05-03 | Honda Motor Co., Ltd. | Detecting Missing Records in Financial Transactions by Applying Business Rules |
US20080005037A1 (en) * | 2006-06-19 | 2008-01-03 | Ayman Hammad | Consumer authentication system and method |
US20080319869A1 (en) * | 2007-06-25 | 2008-12-25 | Mark Carlson | Systems and methods for secure and transparent cardless transactions |
US20080319904A1 (en) * | 2007-06-25 | 2008-12-25 | Mark Carlson | Seeding challenges for payment transactions |
US20090024630A1 (en) * | 2005-06-20 | 2009-01-22 | Kraft Harold H | Privacy Information Reporting Systems with Broad Search Scope and Integration |
US7546271B1 (en) | 2007-12-20 | 2009-06-09 | Choicepoint Asset Company | Mortgage fraud detection systems and methods |
US20100114776A1 (en) * | 2008-11-06 | 2010-05-06 | Kevin Weller | Online challenge-response |
US20100235908A1 (en) * | 2009-03-13 | 2010-09-16 | Silver Tail Systems | System and Method for Detection of a Change in Behavior in the Use of a Website Through Vector Analysis |
US20100235909A1 (en) * | 2009-03-13 | 2010-09-16 | Silver Tail Systems | System and Method for Detection of a Change in Behavior in the Use of a Website Through Vector Velocity Analysis |
US8571937B2 (en) | 2010-10-20 | 2013-10-29 | Playspan Inc. | Dynamic payment optimization apparatuses, methods and systems |
US8577803B2 (en) | 2011-06-03 | 2013-11-05 | Visa International Service Association | Virtual wallet card selection apparatuses, methods and systems |
US20140143010A1 (en) * | 2012-11-16 | 2014-05-22 | SPF, Inc. | System and Method for Assessing Interaction Risks Potentially Associated with Transactions Between a Client and a Provider |
US8788407B1 (en) | 2013-03-15 | 2014-07-22 | Palantir Technologies Inc. | Malware data clustering |
US8817984B2 (en) | 2011-02-03 | 2014-08-26 | mSignia, Inc. | Cryptographic security functions based on anticipated changes in dynamic minutiae |
US8855999B1 (en) | 2013-03-15 | 2014-10-07 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
US8930897B2 (en) | 2013-03-15 | 2015-01-06 | Palantir Technologies Inc. | Data integration tool |
US8973102B2 (en) | 2012-06-14 | 2015-03-03 | Ebay Inc. | Systems and methods for authenticating a user and device |
US9009827B1 (en) | 2014-02-20 | 2015-04-14 | Palantir Technologies Inc. | Security sharing system |
US9021260B1 (en) | 2014-07-03 | 2015-04-28 | Palantir Technologies Inc. | Malware data item analysis |
US9043894B1 (en) | 2014-11-06 | 2015-05-26 | Palantir Technologies Inc. | Malicious software detection in a computing system |
US9117225B2 (en) | 2011-09-16 | 2015-08-25 | Visa International Service Association | Apparatuses, methods and systems for transforming user infrastructure requests inputs to infrastructure design product and infrastructure allocation outputs |
US9202249B1 (en) | 2014-07-03 | 2015-12-01 | Palantir Technologies Inc. | Data item clustering and analysis |
US9230280B1 (en) | 2013-03-15 | 2016-01-05 | Palantir Technologies Inc. | Clustering data based on indications of financial malfeasance |
US9355393B2 (en) | 2011-08-18 | 2016-05-31 | Visa International Service Association | Multi-directional wallet connector apparatuses, methods and systems |
US9361597B2 (en) | 2010-10-19 | 2016-06-07 | The 41St Parameter, Inc. | Variable risk engine |
US9367872B1 (en) | 2014-12-22 | 2016-06-14 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures |
US9454785B1 (en) | 2015-07-30 | 2016-09-27 | Palantir Technologies Inc. | Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data |
US9521551B2 (en) | 2012-03-22 | 2016-12-13 | The 41St Parameter, Inc. | Methods and systems for persistent cross-application mobile device identification |
US9535974B1 (en) | 2014-06-30 | 2017-01-03 | Palantir Technologies Inc. | Systems and methods for identifying key phrase clusters within documents |
US9552615B2 (en) | 2013-12-20 | 2017-01-24 | Palantir Technologies Inc. | Automated database analysis to detect malfeasance |
US9633201B1 (en) | 2012-03-01 | 2017-04-25 | The 41St Parameter, Inc. | Methods and systems for fraud containment |
US9635046B2 (en) | 2015-08-06 | 2017-04-25 | Palantir Technologies Inc. | Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications |
US9646291B2 (en) | 2011-05-11 | 2017-05-09 | Visa International Service Association | Electronic receipt manager apparatuses, methods and systems |
US9652765B2 (en) | 2008-08-26 | 2017-05-16 | Visa International Service Association | System and method for implementing financial assistance programs |
US9703983B2 (en) | 2005-12-16 | 2017-07-11 | The 41St Parameter, Inc. | Methods and apparatus for securely displaying digital images |
US9710807B2 (en) | 2011-08-18 | 2017-07-18 | Visa International Service Association | Third-party value added wallet features and interfaces apparatuses, methods and systems |
US9747639B1 (en) * | 2010-09-01 | 2017-08-29 | Federal Home Loan Mortgage Corporation (Freddie Mac) | Systems and methods for measuring data quality over time |
US9754311B2 (en) | 2006-03-31 | 2017-09-05 | The 41St Parameter, Inc. | Systems and methods for detection of session tampering and fraud prevention |
US9773212B2 (en) | 2011-02-28 | 2017-09-26 | Visa International Service Association | Secure anonymous transaction apparatuses, methods and systems |
US9785773B2 (en) | 2014-07-03 | 2017-10-10 | Palantir Technologies Inc. | Malware data item analysis |
US9817563B1 (en) | 2014-12-29 | 2017-11-14 | Palantir Technologies Inc. | System and method of generating data points from one or more data stores of data items for chart creation and manipulation |
US9830328B2 (en) | 2012-02-02 | 2017-11-28 | Visa International Service Association | Multi-source, multi-dimensional, cross-entry, multimedia merchant analytics database platform apparatuses, methods and systems |
US9875293B2 (en) | 2014-07-03 | 2018-01-23 | Palanter Technologies Inc. | System and method for news events detection and visualization |
US9898509B2 (en) | 2015-08-28 | 2018-02-20 | Palantir Technologies Inc. | Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces |
US9898528B2 (en) | 2014-12-22 | 2018-02-20 | Palantir Technologies Inc. | Concept indexing among database of documents using machine learning techniques |
US9948629B2 (en) | 2009-03-25 | 2018-04-17 | The 41St Parameter, Inc. | Systems and methods of sharing information through a tag-based consortium |
US9953378B2 (en) | 2012-04-27 | 2018-04-24 | Visa International Service Association | Social checkout widget generation and integration apparatuses, methods and systems |
US9953334B2 (en) | 2011-02-10 | 2018-04-24 | Visa International Service Association | Electronic coupon issuance and redemption apparatuses, methods and systems |
US9965937B2 (en) | 2013-03-15 | 2018-05-08 | Palantir Technologies Inc. | External malware data item clustering and analysis |
US9990631B2 (en) | 2012-11-14 | 2018-06-05 | The 41St Parameter, Inc. | Systems and methods of global identification |
US9996838B2 (en) | 2011-03-04 | 2018-06-12 | Visa International Service Association | Cloud service facilitator apparatuses, methods and systems |
US10091312B1 (en) | 2014-10-14 | 2018-10-02 | The 41St Parameter, Inc. | Data structures for intelligently resolving deterministic and probabilistic device identifiers to device profiles and/or groups |
US10096022B2 (en) | 2011-12-13 | 2018-10-09 | Visa International Service Association | Dynamic widget generator apparatuses, methods and systems |
US10103953B1 (en) | 2015-05-12 | 2018-10-16 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10121129B2 (en) | 2011-07-05 | 2018-11-06 | Visa International Service Association | Electronic wallet checkout platform apparatuses, methods and systems |
US10120857B2 (en) | 2013-03-15 | 2018-11-06 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
US10154084B2 (en) | 2011-07-05 | 2018-12-11 | Visa International Service Association | Hybrid applications utilizing distributed models and views apparatuses, methods and systems |
US10162887B2 (en) | 2014-06-30 | 2018-12-25 | Palantir Technologies Inc. | Systems and methods for key phrase characterization of documents |
US10204327B2 (en) | 2011-02-05 | 2019-02-12 | Visa International Service Association | Merchant-consumer bridging platform apparatuses, methods and systems |
US10223730B2 (en) | 2011-09-23 | 2019-03-05 | Visa International Service Association | E-wallet store injection search apparatuses, methods and systems |
US10223691B2 (en) | 2011-02-22 | 2019-03-05 | Visa International Service Association | Universal electronic payment apparatuses, methods and systems |
US10223710B2 (en) | 2013-01-04 | 2019-03-05 | Visa International Service Association | Wearable intelligent vision device apparatuses, methods and systems |
US10230746B2 (en) | 2014-01-03 | 2019-03-12 | Palantir Technologies Inc. | System and method for evaluating network threats and usage |
US10235461B2 (en) | 2017-05-02 | 2019-03-19 | Palantir Technologies Inc. | Automated assistance for generating relevant and valuable search results for an entity of interest |
US10242358B2 (en) | 2011-08-18 | 2019-03-26 | Visa International Service Association | Remote decoupled application persistent state apparatuses, methods and systems |
US10262148B2 (en) | 2012-01-09 | 2019-04-16 | Visa International Service Association | Secure dynamic page content and layouts apparatuses, methods and systems |
US10275778B1 (en) | 2013-03-15 | 2019-04-30 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation based on automatic malfeasance clustering of related data in various data structures |
US10318630B1 (en) | 2016-11-21 | 2019-06-11 | Palantir Technologies Inc. | Analysis of large bodies of textual data |
US10318941B2 (en) | 2011-12-13 | 2019-06-11 | Visa International Service Association | Payment platform interface widget generation apparatuses, methods and systems |
US10325224B1 (en) | 2017-03-23 | 2019-06-18 | Palantir Technologies Inc. | Systems and methods for selecting machine learning training data |
US10356032B2 (en) | 2013-12-26 | 2019-07-16 | Palantir Technologies Inc. | System and method for detecting confidential information emails |
US10362133B1 (en) | 2014-12-22 | 2019-07-23 | Palantir Technologies Inc. | Communication data processing architecture |
US10366360B2 (en) | 2012-11-16 | 2019-07-30 | SPF, Inc. | System and method for identifying potential future interaction risks between a client and a provider |
US10417637B2 (en) | 2012-08-02 | 2019-09-17 | The 41St Parameter, Inc. | Systems and methods for accessing records via derivative locators |
US10438176B2 (en) | 2011-07-17 | 2019-10-08 | Visa International Service Association | Multiple merchant payment processor platform apparatuses, methods and systems |
US10453066B2 (en) | 2003-07-01 | 2019-10-22 | The 41St Parameter, Inc. | Keystroke analysis |
US10482382B2 (en) | 2017-05-09 | 2019-11-19 | Palantir Technologies Inc. | Systems and methods for reducing manufacturing failure rates |
US10489391B1 (en) | 2015-08-17 | 2019-11-26 | Palantir Technologies Inc. | Systems and methods for grouping and enriching data items accessed from one or more databases for presentation in a user interface |
US10552994B2 (en) | 2014-12-22 | 2020-02-04 | Palantir Technologies Inc. | Systems and interactive user interfaces for dynamic retrieval, analysis, and triage of data items |
US10572496B1 (en) | 2014-07-03 | 2020-02-25 | Palantir Technologies Inc. | Distributed workflow system and database with access controls for city resiliency |
US10572487B1 (en) | 2015-10-30 | 2020-02-25 | Palantir Technologies Inc. | Periodic database search manager for multiple data sources |
US10579647B1 (en) | 2013-12-16 | 2020-03-03 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US10586227B2 (en) | 2011-02-16 | 2020-03-10 | Visa International Service Association | Snap mobile payment apparatuses, methods and systems |
US10606866B1 (en) | 2017-03-30 | 2020-03-31 | Palantir Technologies Inc. | Framework for exposing network activities |
US10620618B2 (en) | 2016-12-20 | 2020-04-14 | Palantir Technologies Inc. | Systems and methods for determining relationships between defects |
US10719527B2 (en) | 2013-10-18 | 2020-07-21 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive simultaneous querying of multiple data stores |
US10825001B2 (en) | 2011-08-18 | 2020-11-03 | Visa International Service Association | Multi-directional wallet connector apparatuses, methods and systems |
US10838987B1 (en) | 2017-12-20 | 2020-11-17 | Palantir Technologies Inc. | Adaptive and transparent entity screening |
US10902327B1 (en) | 2013-08-30 | 2021-01-26 | The 41St Parameter, Inc. | System and method for device identification and uniqueness |
US10999298B2 (en) | 2004-03-02 | 2021-05-04 | The 41St Parameter, Inc. | Method and system for identifying users and detecting fraud by use of the internet |
US11063920B2 (en) | 2011-02-03 | 2021-07-13 | mSignia, Inc. | Cryptographic security functions based on anticipated changes in dynamic minutiae |
US11119630B1 (en) | 2018-06-19 | 2021-09-14 | Palantir Technologies Inc. | Artificial intelligence assisted evaluations and user interface for same |
US11164206B2 (en) * | 2018-11-16 | 2021-11-02 | Comenity Llc | Automatically aggregating, evaluating, and providing a contextually relevant offer |
US11216468B2 (en) | 2015-02-08 | 2022-01-04 | Visa International Service Association | Converged merchant processing apparatuses, methods and systems |
US11288661B2 (en) | 2011-02-16 | 2022-03-29 | Visa International Service Association | Snap mobile payment apparatuses, methods and systems |
US11301585B2 (en) | 2005-12-16 | 2022-04-12 | The 41St Parameter, Inc. | Methods and apparatus for securely displaying digital images |
US11308227B2 (en) | 2012-01-09 | 2022-04-19 | Visa International Service Association | Secure dynamic page content and layouts apparatuses, methods and systems |
US11314838B2 (en) | 2011-11-15 | 2022-04-26 | Tapad, Inc. | System and method for analyzing user device information |
Families Citing this family (23)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8392418B2 (en) * | 2009-06-25 | 2013-03-05 | University Of Tennessee Research Foundation | Method and apparatus for predicting object properties and events using similarity-based information retrieval and model |
GB0618627D0 (en) * | 2006-09-21 | 2006-11-01 | Vodafone Ltd | Fraud detection system |
US7631019B2 (en) * | 2007-05-31 | 2009-12-08 | Red Hat, Inc. | Distributing data across different backing data stores |
CA2689072C (en) * | 2007-12-05 | 2018-01-09 | Bce Inc. | Methods and computer-readable media for facilitating forensic investigations of online transactions |
US8032551B2 (en) * | 2009-05-11 | 2011-10-04 | Red Hat, Inc. | Searching documents for successive hashed keywords |
US8032550B2 (en) * | 2009-05-11 | 2011-10-04 | Red Hat, Inc. | Federated document search by keywords |
US8037076B2 (en) * | 2009-05-11 | 2011-10-11 | Red Hat, Inc. | Federated indexing from hashed primary key slices |
US8739125B2 (en) * | 2009-06-16 | 2014-05-27 | Red Hat, Inc. | Automated and unattended process for testing software applications |
US8396870B2 (en) * | 2009-06-25 | 2013-03-12 | University Of Tennessee Research Foundation | Method and apparatus for predicting object properties and events using similarity-based information retrieval and modeling |
US11797997B2 (en) * | 2009-07-07 | 2023-10-24 | Visa International Service Association | Data verification in transactions in distributed network |
EP2452303A4 (en) * | 2009-07-07 | 2016-07-06 | Finsphere Corp | Mobile directory number and email verification of financial transactions |
US8458069B2 (en) * | 2011-03-04 | 2013-06-04 | Brighterion, Inc. | Systems and methods for adaptive identification of sources of fraud |
US11624822B2 (en) * | 2011-10-26 | 2023-04-11 | Teledyne Flir, Llc | Pilot display systems and methods |
US9386078B2 (en) * | 2014-05-30 | 2016-07-05 | Ca, Inc. | Controlling application programming interface transactions based on content of earlier transactions |
US20160019297A1 (en) * | 2014-07-15 | 2016-01-21 | Datagence Inc. | Method and apparatus for cloning a target list |
US10203952B2 (en) | 2015-11-18 | 2019-02-12 | Sap Se | Transparently splitting and rewriting data-base object bundles to database entities |
US10417216B2 (en) * | 2015-11-19 | 2019-09-17 | Sap Se | Determining an intersection between keys defining multi-dimensional value ranges |
US10074362B2 (en) | 2017-01-31 | 2018-09-11 | Global Tel*Link Corporation | System and method for assessing security threats and criminal proclivities |
US10129392B1 (en) | 2017-08-25 | 2018-11-13 | Global Tel*Link Corporation | Systems and methods for detecting inmate to inmate conference calls |
US11143789B2 (en) * | 2017-10-11 | 2021-10-12 | Beyond Limits, Inc. | Static engine and neural network for a cognitive reservoir system |
EP3651100A1 (en) * | 2018-11-09 | 2020-05-13 | MasterCard International Incorporated | Anomaly detection method for financial transactions |
WO2020176977A1 (en) * | 2019-03-01 | 2020-09-10 | Mastercard Technologies Canada ULC | Multi-page online application origination (oao) service for fraud prevention systems |
WO2021062545A1 (en) | 2019-10-01 | 2021-04-08 | Mastercard Technologies Canada ULC | Feature encoding in online application origination (oao) service for a fraud prevention system |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6523019B1 (en) * | 1999-09-21 | 2003-02-18 | Choicemaker Technologies, Inc. | Probabilistic record linkage model derived from training data |
US20030154214A1 (en) * | 2002-02-06 | 2003-08-14 | Junh-Hsien Tu | Automatic storage and retrieval system and method for operating the same |
US20040030912A1 (en) * | 2001-05-09 | 2004-02-12 | Merkle James A. | Systems and methods for the prevention of unauthorized use and manipulation of digital content |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CA2425488A1 (en) * | 2000-10-12 | 2002-04-18 | Iconix Pharmaceuticals, Inc | Interactive correlation of compound information and genomic information |
-
2003
- 2003-08-13 US US10/640,826 patent/US7373669B2/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6523019B1 (en) * | 1999-09-21 | 2003-02-18 | Choicemaker Technologies, Inc. | Probabilistic record linkage model derived from training data |
US20040030912A1 (en) * | 2001-05-09 | 2004-02-12 | Merkle James A. | Systems and methods for the prevention of unauthorized use and manipulation of digital content |
US20030154214A1 (en) * | 2002-02-06 | 2003-08-14 | Junh-Hsien Tu | Automatic storage and retrieval system and method for operating the same |
Cited By (228)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11238456B2 (en) | 2003-07-01 | 2022-02-01 | The 41St Parameter, Inc. | Keystroke analysis |
US10453066B2 (en) | 2003-07-01 | 2019-10-22 | The 41St Parameter, Inc. | Keystroke analysis |
US11683326B2 (en) | 2004-03-02 | 2023-06-20 | The 41St Parameter, Inc. | Method and system for identifying users and detecting fraud by use of the internet |
US10999298B2 (en) | 2004-03-02 | 2021-05-04 | The 41St Parameter, Inc. | Method and system for identifying users and detecting fraud by use of the internet |
US20090024630A1 (en) * | 2005-06-20 | 2009-01-22 | Kraft Harold H | Privacy Information Reporting Systems with Broad Search Scope and Integration |
US8540140B2 (en) | 2005-09-02 | 2013-09-24 | Honda Motor Co., Ltd. | Automated handling of exceptions in financial transaction records |
US8095437B2 (en) * | 2005-09-02 | 2012-01-10 | Honda Motor Co., Ltd. | Detecting missing files in financial transactions by applying business rules |
US20070100716A1 (en) * | 2005-09-02 | 2007-05-03 | Honda Motor Co., Ltd. | Financial Transaction Controls Using Sending And Receiving Control Data |
US20070100717A1 (en) * | 2005-09-02 | 2007-05-03 | Honda Motor Co., Ltd. | Detecting Missing Records in Financial Transactions by Applying Business Rules |
US20070069006A1 (en) * | 2005-09-02 | 2007-03-29 | Honda Motor Co., Ltd. | Automated Handling of Exceptions in Financial Transaction Records |
US8099340B2 (en) | 2005-09-02 | 2012-01-17 | Honda Motor Co., Ltd. | Financial transaction controls using sending and receiving control data |
US10726151B2 (en) | 2005-12-16 | 2020-07-28 | The 41St Parameter, Inc. | Methods and apparatus for securely displaying digital images |
US11301585B2 (en) | 2005-12-16 | 2022-04-12 | The 41St Parameter, Inc. | Methods and apparatus for securely displaying digital images |
US9703983B2 (en) | 2005-12-16 | 2017-07-11 | The 41St Parameter, Inc. | Methods and apparatus for securely displaying digital images |
US10535093B2 (en) | 2006-03-31 | 2020-01-14 | The 41St Parameter, Inc. | Systems and methods for detection of session tampering and fraud prevention |
US11727471B2 (en) | 2006-03-31 | 2023-08-15 | The 41St Parameter, Inc. | Systems and methods for detection of session tampering and fraud prevention |
US11195225B2 (en) | 2006-03-31 | 2021-12-07 | The 41St Parameter, Inc. | Systems and methods for detection of session tampering and fraud prevention |
US9754311B2 (en) | 2006-03-31 | 2017-09-05 | The 41St Parameter, Inc. | Systems and methods for detection of session tampering and fraud prevention |
US10089679B2 (en) | 2006-03-31 | 2018-10-02 | The 41St Parameter, Inc. | Systems and methods for detection of session tampering and fraud prevention |
US20080005037A1 (en) * | 2006-06-19 | 2008-01-03 | Ayman Hammad | Consumer authentication system and method |
US8135647B2 (en) | 2006-06-19 | 2012-03-13 | Visa U.S.A. Inc. | Consumer authentication system and method |
US10089624B2 (en) | 2006-06-19 | 2018-10-02 | Visa U.S.A. Inc. | Consumer authentication system and method |
US11488150B2 (en) | 2006-06-19 | 2022-11-01 | Visa U.S.A. Inc. | Consumer authentication system and method |
US11107069B2 (en) | 2006-06-19 | 2021-08-31 | Visa U.S.A. Inc. | Transaction authentication using network |
US11783326B2 (en) | 2006-06-19 | 2023-10-10 | Visa U.S.A. Inc. | Transaction authentication using network |
US8121956B2 (en) | 2007-06-25 | 2012-02-21 | Visa U.S.A. Inc. | Cardless challenge systems and methods |
US8606700B2 (en) | 2007-06-25 | 2013-12-10 | Visa U.S.A., Inc. | Systems and methods for secure and transparent cardless transactions |
US8744958B2 (en) | 2007-06-25 | 2014-06-03 | Visa U. S. A. Inc. | Systems and methods for secure and transparent cardless transactions |
US10262308B2 (en) | 2007-06-25 | 2019-04-16 | Visa U.S.A. Inc. | Cardless challenge systems and methods |
US8706621B2 (en) | 2007-06-25 | 2014-04-22 | Visa U.S.A., Inc. | Secure checkout and challenge systems and methods |
US11481742B2 (en) | 2007-06-25 | 2022-10-25 | Visa U.S.A. Inc. | Cardless challenge systems and methods |
US8380629B2 (en) | 2007-06-25 | 2013-02-19 | Visa U.S.A. Inc. | Seeding challenges for payment transactions |
US20080319904A1 (en) * | 2007-06-25 | 2008-12-25 | Mark Carlson | Seeding challenges for payment transactions |
US8589291B2 (en) | 2007-06-25 | 2013-11-19 | Visa U.S.A. Inc. | System and method utilizing device information |
US8121942B2 (en) | 2007-06-25 | 2012-02-21 | Visa U.S.A. Inc. | Systems and methods for secure and transparent cardless transactions |
US20080319869A1 (en) * | 2007-06-25 | 2008-12-25 | Mark Carlson | Systems and methods for secure and transparent cardless transactions |
US20090164232A1 (en) * | 2007-12-20 | 2009-06-25 | Choicepoint Asset Company | Mortgage fraud detection systems and methods |
US7546271B1 (en) | 2007-12-20 | 2009-06-09 | Choicepoint Asset Company | Mortgage fraud detection systems and methods |
US20100241558A1 (en) * | 2007-12-20 | 2010-09-23 | LexisNexis, Inc. | Mortgage fraud detection systems and methods |
US9652765B2 (en) | 2008-08-26 | 2017-05-16 | Visa International Service Association | System and method for implementing financial assistance programs |
US8762279B2 (en) | 2008-11-06 | 2014-06-24 | Visa International Service Association | Online challenge-response |
US20100114776A1 (en) * | 2008-11-06 | 2010-05-06 | Kevin Weller | Online challenge-response |
US8533118B2 (en) | 2008-11-06 | 2013-09-10 | Visa International Service Association | Online challenge-response |
US9898740B2 (en) | 2008-11-06 | 2018-02-20 | Visa International Service Association | Online challenge-response |
US20100235908A1 (en) * | 2009-03-13 | 2010-09-16 | Silver Tail Systems | System and Method for Detection of a Change in Behavior in the Use of a Website Through Vector Analysis |
US20100235909A1 (en) * | 2009-03-13 | 2010-09-16 | Silver Tail Systems | System and Method for Detection of a Change in Behavior in the Use of a Website Through Vector Velocity Analysis |
US9948629B2 (en) | 2009-03-25 | 2018-04-17 | The 41St Parameter, Inc. | Systems and methods of sharing information through a tag-based consortium |
US11750584B2 (en) | 2009-03-25 | 2023-09-05 | The 41St Parameter, Inc. | Systems and methods of sharing information through a tag-based consortium |
US10616201B2 (en) | 2009-03-25 | 2020-04-07 | The 41St Parameter, Inc. | Systems and methods of sharing information through a tag-based consortium |
US11017467B1 (en) * | 2010-09-01 | 2021-05-25 | Federal Home Loan Mortgage Corporation (Freddie Mac) | Systems and methods for measuring data quality over time |
US11556983B1 (en) | 2010-09-01 | 2023-01-17 | Federal Home Loan Mortgage Corporation (Freddie Mac) | Systems and methods for measuring data quality over time |
US9747639B1 (en) * | 2010-09-01 | 2017-08-29 | Federal Home Loan Mortgage Corporation (Freddie Mac) | Systems and methods for measuring data quality over time |
US9361597B2 (en) | 2010-10-19 | 2016-06-07 | The 41St Parameter, Inc. | Variable risk engine |
US9754256B2 (en) | 2010-10-19 | 2017-09-05 | The 41St Parameter, Inc. | Variable risk engine |
US10688385B2 (en) | 2010-10-20 | 2020-06-23 | Playspan Inc. | In-application universal storefront apparatuses, methods and systems |
US8571937B2 (en) | 2010-10-20 | 2013-10-29 | Playspan Inc. | Dynamic payment optimization apparatuses, methods and systems |
US10500481B2 (en) | 2010-10-20 | 2019-12-10 | Playspan Inc. | Dynamic payment optimization apparatuses, methods and systems |
US11311797B2 (en) | 2010-10-20 | 2022-04-26 | Playspan Inc. | Dynamic payment optimization apparatuses, methods and systems |
US9757644B2 (en) | 2010-10-20 | 2017-09-12 | Playspin Inc. | Dynamic payment optimization apparatuses, methods and systems |
US11063920B2 (en) | 2011-02-03 | 2021-07-13 | mSignia, Inc. | Cryptographic security functions based on anticipated changes in dynamic minutiae |
US9559852B2 (en) | 2011-02-03 | 2017-01-31 | mSignia, Inc. | Cryptographic security functions based on anticipated changes in dynamic minutiae |
US9979707B2 (en) | 2011-02-03 | 2018-05-22 | mSignia, Inc. | Cryptographic security functions based on anticipated changes in dynamic minutiae |
US8817984B2 (en) | 2011-02-03 | 2014-08-26 | mSignia, Inc. | Cryptographic security functions based on anticipated changes in dynamic minutiae |
US9294448B2 (en) | 2011-02-03 | 2016-03-22 | mSignia, Inc. | Cryptographic security functions based on anticipated changes in dynamic minutiae |
US10178076B2 (en) | 2011-02-03 | 2019-01-08 | mSignia, Inc. | Cryptographic security functions based on anticipated changes in dynamic minutiae |
US9722804B2 (en) | 2011-02-03 | 2017-08-01 | mSignia, Inc. | Cryptographic security functions based on anticipated changes in dynamic minutiae |
US10204327B2 (en) | 2011-02-05 | 2019-02-12 | Visa International Service Association | Merchant-consumer bridging platform apparatuses, methods and systems |
US11093919B2 (en) | 2011-02-05 | 2021-08-17 | Visa International Service Association | Merchant-consumer bridging platform apparatuses, methods and systems |
US9953334B2 (en) | 2011-02-10 | 2018-04-24 | Visa International Service Association | Electronic coupon issuance and redemption apparatuses, methods and systems |
US10621605B2 (en) | 2011-02-10 | 2020-04-14 | Visa International Service Association | Electronic coupon issuance and redemption apparatuses, methods and systems |
US11288661B2 (en) | 2011-02-16 | 2022-03-29 | Visa International Service Association | Snap mobile payment apparatuses, methods and systems |
US10586227B2 (en) | 2011-02-16 | 2020-03-10 | Visa International Service Association | Snap mobile payment apparatuses, methods and systems |
US10223691B2 (en) | 2011-02-22 | 2019-03-05 | Visa International Service Association | Universal electronic payment apparatuses, methods and systems |
US11023886B2 (en) | 2011-02-22 | 2021-06-01 | Visa International Service Association | Universal electronic payment apparatuses, methods and systems |
US10482398B2 (en) | 2011-02-28 | 2019-11-19 | Visa International Service Association | Secure anonymous transaction apparatuses, methods and systems |
US11250352B2 (en) | 2011-02-28 | 2022-02-15 | Visa International Service Association | Secure anonymous transaction apparatuses, methods and systems |
US9773212B2 (en) | 2011-02-28 | 2017-09-26 | Visa International Service Association | Secure anonymous transaction apparatuses, methods and systems |
US11263640B2 (en) | 2011-03-04 | 2022-03-01 | Visa International Service Association | Cloud service facilitator apparatuses, methods and systems |
US9996838B2 (en) | 2011-03-04 | 2018-06-12 | Visa International Service Association | Cloud service facilitator apparatuses, methods and systems |
US10489756B2 (en) | 2011-05-11 | 2019-11-26 | Visa International Service Association | Electronic receipt manager apparatuses, methods and systems |
US11263601B2 (en) | 2011-05-11 | 2022-03-01 | Visa International Service Association | Electronic receipt manager apparatuses, methods and systems |
US11853977B2 (en) | 2011-05-11 | 2023-12-26 | Visa International Service Association | Electronic receipt manager apparatuses, methods and systems |
US9646291B2 (en) | 2011-05-11 | 2017-05-09 | Visa International Service Association | Electronic receipt manager apparatuses, methods and systems |
US8577803B2 (en) | 2011-06-03 | 2013-11-05 | Visa International Service Association | Virtual wallet card selection apparatuses, methods and systems |
US10154084B2 (en) | 2011-07-05 | 2018-12-11 | Visa International Service Association | Hybrid applications utilizing distributed models and views apparatuses, methods and systems |
US11010753B2 (en) | 2011-07-05 | 2021-05-18 | Visa International Service Association | Electronic wallet checkout platform apparatuses, methods and systems |
US10419529B2 (en) | 2011-07-05 | 2019-09-17 | Visa International Service Association | Hybrid applications utilizing distributed models and views apparatuses, methods and systems |
US10803449B2 (en) | 2011-07-05 | 2020-10-13 | Visa International Service Association | Electronic wallet checkout platform apparatuses, methods and systems |
US10121129B2 (en) | 2011-07-05 | 2018-11-06 | Visa International Service Association | Electronic wallet checkout platform apparatuses, methods and systems |
US11900359B2 (en) | 2011-07-05 | 2024-02-13 | Visa International Service Association | Electronic wallet checkout platform apparatuses, methods and systems |
US10438176B2 (en) | 2011-07-17 | 2019-10-08 | Visa International Service Association | Multiple merchant payment processor platform apparatuses, methods and systems |
US11010756B2 (en) | 2011-08-18 | 2021-05-18 | Visa International Service Association | Remote decoupled application persistent state apparatuses, methods and systems |
US10825001B2 (en) | 2011-08-18 | 2020-11-03 | Visa International Service Association | Multi-directional wallet connector apparatuses, methods and systems |
US9355393B2 (en) | 2011-08-18 | 2016-05-31 | Visa International Service Association | Multi-directional wallet connector apparatuses, methods and systems |
US10242358B2 (en) | 2011-08-18 | 2019-03-26 | Visa International Service Association | Remote decoupled application persistent state apparatuses, methods and systems |
US11803825B2 (en) | 2011-08-18 | 2023-10-31 | Visa International Service Association | Multi-directional wallet connector apparatuses, methods and systems |
US10354240B2 (en) | 2011-08-18 | 2019-07-16 | Visa International Service Association | Multi-directional wallet connector apparatuses, methods and systems |
US9959531B2 (en) | 2011-08-18 | 2018-05-01 | Visa International Service Association | Multi-directional wallet connector apparatuses, methods and systems |
US11763294B2 (en) | 2011-08-18 | 2023-09-19 | Visa International Service Association | Remote decoupled application persistent state apparatuses, methods and systems |
US11037138B2 (en) | 2011-08-18 | 2021-06-15 | Visa International Service Association | Third-party value added wallet features and interfaces apparatuses, methods, and systems |
US9710807B2 (en) | 2011-08-18 | 2017-07-18 | Visa International Service Association | Third-party value added wallet features and interfaces apparatuses, methods and systems |
US11397931B2 (en) | 2011-08-18 | 2022-07-26 | Visa International Service Association | Multi-directional wallet connector apparatuses, methods and systems |
US9117225B2 (en) | 2011-09-16 | 2015-08-25 | Visa International Service Association | Apparatuses, methods and systems for transforming user infrastructure requests inputs to infrastructure design product and infrastructure allocation outputs |
US10223730B2 (en) | 2011-09-23 | 2019-03-05 | Visa International Service Association | E-wallet store injection search apparatuses, methods and systems |
US11354723B2 (en) | 2011-09-23 | 2022-06-07 | Visa International Service Association | Smart shopping cart with E-wallet store injection search |
US11314838B2 (en) | 2011-11-15 | 2022-04-26 | Tapad, Inc. | System and method for analyzing user device information |
US10846670B2 (en) | 2011-12-13 | 2020-11-24 | Visa International Service Association | Payment platform interface widget generation apparatuses, methods and systems |
US10318941B2 (en) | 2011-12-13 | 2019-06-11 | Visa International Service Association | Payment platform interface widget generation apparatuses, methods and systems |
US10096022B2 (en) | 2011-12-13 | 2018-10-09 | Visa International Service Association | Dynamic widget generator apparatuses, methods and systems |
US10685379B2 (en) | 2012-01-05 | 2020-06-16 | Visa International Service Association | Wearable intelligent vision device apparatuses, methods and systems |
US11308227B2 (en) | 2012-01-09 | 2022-04-19 | Visa International Service Association | Secure dynamic page content and layouts apparatuses, methods and systems |
US10262148B2 (en) | 2012-01-09 | 2019-04-16 | Visa International Service Association | Secure dynamic page content and layouts apparatuses, methods and systems |
US10262001B2 (en) | 2012-02-02 | 2019-04-16 | Visa International Service Association | Multi-source, multi-dimensional, cross-entity, multimedia merchant analytics database platform apparatuses, methods and systems |
US11074218B2 (en) | 2012-02-02 | 2021-07-27 | Visa International Service Association | Multi-source, multi-dimensional, cross-entity, multimedia merchant analytics database platform apparatuses, methods and systems |
US10430381B2 (en) | 2012-02-02 | 2019-10-01 | Visa International Service Association | Multi-source, multi-dimensional, cross-entity, multimedia centralized personal information database platform apparatuses, methods and systems |
US11036681B2 (en) | 2012-02-02 | 2021-06-15 | Visa International Service Association | Multi-source, multi-dimensional, cross-entity, multimedia analytical model sharing database platform apparatuses, methods and systems |
US10983960B2 (en) | 2012-02-02 | 2021-04-20 | Visa International Service Association | Multi-source, multi-dimensional, cross-entity, multimedia centralized personal information database platform apparatuses, methods and systems |
US10013423B2 (en) | 2012-02-02 | 2018-07-03 | Visa International Service Association | Multi-source, multi-dimensional, cross-entity, multimedia analytical model sharing database platform apparatuses, methods and systems |
US9830328B2 (en) | 2012-02-02 | 2017-11-28 | Visa International Service Association | Multi-source, multi-dimensional, cross-entry, multimedia merchant analytics database platform apparatuses, methods and systems |
US11886575B1 (en) | 2012-03-01 | 2024-01-30 | The 41St Parameter, Inc. | Methods and systems for fraud containment |
US9633201B1 (en) | 2012-03-01 | 2017-04-25 | The 41St Parameter, Inc. | Methods and systems for fraud containment |
US11010468B1 (en) | 2012-03-01 | 2021-05-18 | The 41St Parameter, Inc. | Methods and systems for fraud containment |
US10341344B2 (en) | 2012-03-22 | 2019-07-02 | The 41St Parameter, Inc. | Methods and systems for persistent cross-application mobile device identification |
US9521551B2 (en) | 2012-03-22 | 2016-12-13 | The 41St Parameter, Inc. | Methods and systems for persistent cross-application mobile device identification |
US10862889B2 (en) | 2012-03-22 | 2020-12-08 | The 41St Parameter, Inc. | Methods and systems for persistent cross application mobile device identification |
US10021099B2 (en) | 2012-03-22 | 2018-07-10 | The 41st Paramter, Inc. | Methods and systems for persistent cross-application mobile device identification |
US11683306B2 (en) | 2012-03-22 | 2023-06-20 | The 41St Parameter, Inc. | Methods and systems for persistent cross-application mobile device identification |
US9953378B2 (en) | 2012-04-27 | 2018-04-24 | Visa International Service Association | Social checkout widget generation and integration apparatuses, methods and systems |
US8973102B2 (en) | 2012-06-14 | 2015-03-03 | Ebay Inc. | Systems and methods for authenticating a user and device |
US9396317B2 (en) | 2012-06-14 | 2016-07-19 | Paypal, Inc. | Systems and methods for authenticating a user and device |
US11301860B2 (en) | 2012-08-02 | 2022-04-12 | The 41St Parameter, Inc. | Systems and methods for accessing records via derivative locators |
US10417637B2 (en) | 2012-08-02 | 2019-09-17 | The 41St Parameter, Inc. | Systems and methods for accessing records via derivative locators |
US10395252B2 (en) | 2012-11-14 | 2019-08-27 | The 41St Parameter, Inc. | Systems and methods of global identification |
US11410179B2 (en) | 2012-11-14 | 2022-08-09 | The 41St Parameter, Inc. | Systems and methods of global identification |
US11922423B2 (en) | 2012-11-14 | 2024-03-05 | The 41St Parameter, Inc. | Systems and methods of global identification |
US10853813B2 (en) | 2012-11-14 | 2020-12-01 | The 41St Parameter, Inc. | Systems and methods of global identification |
US9990631B2 (en) | 2012-11-14 | 2018-06-05 | The 41St Parameter, Inc. | Systems and methods of global identification |
US20140143010A1 (en) * | 2012-11-16 | 2014-05-22 | SPF, Inc. | System and Method for Assessing Interaction Risks Potentially Associated with Transactions Between a Client and a Provider |
US10366360B2 (en) | 2012-11-16 | 2019-07-30 | SPF, Inc. | System and method for identifying potential future interaction risks between a client and a provider |
US10223710B2 (en) | 2013-01-04 | 2019-03-05 | Visa International Service Association | Wearable intelligent vision device apparatuses, methods and systems |
US9965937B2 (en) | 2013-03-15 | 2018-05-08 | Palantir Technologies Inc. | External malware data item clustering and analysis |
US10120857B2 (en) | 2013-03-15 | 2018-11-06 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
US9165299B1 (en) | 2013-03-15 | 2015-10-20 | Palantir Technologies Inc. | User-agent data clustering |
US9171334B1 (en) | 2013-03-15 | 2015-10-27 | Palantir Technologies Inc. | Tax data clustering |
US10264014B2 (en) | 2013-03-15 | 2019-04-16 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation based on automatic clustering of related data in various data structures |
US10275778B1 (en) | 2013-03-15 | 2019-04-30 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation based on automatic malfeasance clustering of related data in various data structures |
US8930897B2 (en) | 2013-03-15 | 2015-01-06 | Palantir Technologies Inc. | Data integration tool |
US10216801B2 (en) | 2013-03-15 | 2019-02-26 | Palantir Technologies Inc. | Generating data clusters |
US9177344B1 (en) | 2013-03-15 | 2015-11-03 | Palantir Technologies Inc. | Trend data clustering |
US9230280B1 (en) | 2013-03-15 | 2016-01-05 | Palantir Technologies Inc. | Clustering data based on indications of financial malfeasance |
US9135658B2 (en) | 2013-03-15 | 2015-09-15 | Palantir Technologies Inc. | Generating data clusters |
US8855999B1 (en) | 2013-03-15 | 2014-10-07 | Palantir Technologies Inc. | Method and system for generating a parser and parsing complex data |
US8788407B1 (en) | 2013-03-15 | 2014-07-22 | Palantir Technologies Inc. | Malware data clustering |
US8818892B1 (en) | 2013-03-15 | 2014-08-26 | Palantir Technologies, Inc. | Prioritizing data clusters with customizable scoring strategies |
US8788405B1 (en) | 2013-03-15 | 2014-07-22 | Palantir Technologies, Inc. | Generating data clusters with customizable analysis strategies |
US10902327B1 (en) | 2013-08-30 | 2021-01-26 | The 41St Parameter, Inc. | System and method for device identification and uniqueness |
US11657299B1 (en) | 2013-08-30 | 2023-05-23 | The 41St Parameter, Inc. | System and method for device identification and uniqueness |
US10719527B2 (en) | 2013-10-18 | 2020-07-21 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive simultaneous querying of multiple data stores |
US10579647B1 (en) | 2013-12-16 | 2020-03-03 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US9552615B2 (en) | 2013-12-20 | 2017-01-24 | Palantir Technologies Inc. | Automated database analysis to detect malfeasance |
US10356032B2 (en) | 2013-12-26 | 2019-07-16 | Palantir Technologies Inc. | System and method for detecting confidential information emails |
US10230746B2 (en) | 2014-01-03 | 2019-03-12 | Palantir Technologies Inc. | System and method for evaluating network threats and usage |
US10805321B2 (en) | 2014-01-03 | 2020-10-13 | Palantir Technologies Inc. | System and method for evaluating network threats and usage |
US9009827B1 (en) | 2014-02-20 | 2015-04-14 | Palantir Technologies Inc. | Security sharing system |
US10873603B2 (en) | 2014-02-20 | 2020-12-22 | Palantir Technologies Inc. | Cyber security sharing and identification system |
US9923925B2 (en) | 2014-02-20 | 2018-03-20 | Palantir Technologies Inc. | Cyber security sharing and identification system |
US10162887B2 (en) | 2014-06-30 | 2018-12-25 | Palantir Technologies Inc. | Systems and methods for key phrase characterization of documents |
US11341178B2 (en) | 2014-06-30 | 2022-05-24 | Palantir Technologies Inc. | Systems and methods for key phrase characterization of documents |
US10180929B1 (en) | 2014-06-30 | 2019-01-15 | Palantir Technologies, Inc. | Systems and methods for identifying key phrase clusters within documents |
US9535974B1 (en) | 2014-06-30 | 2017-01-03 | Palantir Technologies Inc. | Systems and methods for identifying key phrase clusters within documents |
US9881074B2 (en) | 2014-07-03 | 2018-01-30 | Palantir Technologies Inc. | System and method for news events detection and visualization |
US9998485B2 (en) | 2014-07-03 | 2018-06-12 | Palantir Technologies, Inc. | Network intrusion data item clustering and analysis |
US10798116B2 (en) | 2014-07-03 | 2020-10-06 | Palantir Technologies Inc. | External malware data item clustering and analysis |
US9785773B2 (en) | 2014-07-03 | 2017-10-10 | Palantir Technologies Inc. | Malware data item analysis |
US9021260B1 (en) | 2014-07-03 | 2015-04-28 | Palantir Technologies Inc. | Malware data item analysis |
US9875293B2 (en) | 2014-07-03 | 2018-01-23 | Palanter Technologies Inc. | System and method for news events detection and visualization |
US9344447B2 (en) | 2014-07-03 | 2016-05-17 | Palantir Technologies Inc. | Internal malware data item clustering and analysis |
US10929436B2 (en) | 2014-07-03 | 2021-02-23 | Palantir Technologies Inc. | System and method for news events detection and visualization |
US10572496B1 (en) | 2014-07-03 | 2020-02-25 | Palantir Technologies Inc. | Distributed workflow system and database with access controls for city resiliency |
US9202249B1 (en) | 2014-07-03 | 2015-12-01 | Palantir Technologies Inc. | Data item clustering and analysis |
US10728350B1 (en) | 2014-10-14 | 2020-07-28 | The 41St Parameter, Inc. | Data structures for intelligently resolving deterministic and probabilistic device identifiers to device profiles and/or groups |
US11240326B1 (en) | 2014-10-14 | 2022-02-01 | The 41St Parameter, Inc. | Data structures for intelligently resolving deterministic and probabilistic device identifiers to device profiles and/or groups |
US11895204B1 (en) | 2014-10-14 | 2024-02-06 | The 41St Parameter, Inc. | Data structures for intelligently resolving deterministic and probabilistic device identifiers to device profiles and/or groups |
US10091312B1 (en) | 2014-10-14 | 2018-10-02 | The 41St Parameter, Inc. | Data structures for intelligently resolving deterministic and probabilistic device identifiers to device profiles and/or groups |
US9558352B1 (en) | 2014-11-06 | 2017-01-31 | Palantir Technologies Inc. | Malicious software detection in a computing system |
US9043894B1 (en) | 2014-11-06 | 2015-05-26 | Palantir Technologies Inc. | Malicious software detection in a computing system |
US10135863B2 (en) | 2014-11-06 | 2018-11-20 | Palantir Technologies Inc. | Malicious software detection in a computing system |
US10728277B2 (en) | 2014-11-06 | 2020-07-28 | Palantir Technologies Inc. | Malicious software detection in a computing system |
US11252248B2 (en) | 2014-12-22 | 2022-02-15 | Palantir Technologies Inc. | Communication data processing architecture |
US10362133B1 (en) | 2014-12-22 | 2019-07-23 | Palantir Technologies Inc. | Communication data processing architecture |
US9898528B2 (en) | 2014-12-22 | 2018-02-20 | Palantir Technologies Inc. | Concept indexing among database of documents using machine learning techniques |
US10552994B2 (en) | 2014-12-22 | 2020-02-04 | Palantir Technologies Inc. | Systems and interactive user interfaces for dynamic retrieval, analysis, and triage of data items |
US9589299B2 (en) | 2014-12-22 | 2017-03-07 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures |
US9367872B1 (en) | 2014-12-22 | 2016-06-14 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures |
US10447712B2 (en) | 2014-12-22 | 2019-10-15 | Palantir Technologies Inc. | Systems and user interfaces for dynamic and interactive investigation of bad actor behavior based on automatic clustering of related data in various data structures |
US9817563B1 (en) | 2014-12-29 | 2017-11-14 | Palantir Technologies Inc. | System and method of generating data points from one or more data stores of data items for chart creation and manipulation |
US10552998B2 (en) | 2014-12-29 | 2020-02-04 | Palantir Technologies Inc. | System and method of generating data points from one or more data stores of data items for chart creation and manipulation |
US11941008B2 (en) | 2015-02-08 | 2024-03-26 | Visa International Service Association | Converged merchant processing apparatuses, methods and systems |
US11216468B2 (en) | 2015-02-08 | 2022-01-04 | Visa International Service Association | Converged merchant processing apparatuses, methods and systems |
US10103953B1 (en) | 2015-05-12 | 2018-10-16 | Palantir Technologies Inc. | Methods and systems for analyzing entity performance |
US9454785B1 (en) | 2015-07-30 | 2016-09-27 | Palantir Technologies Inc. | Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data |
US10223748B2 (en) | 2015-07-30 | 2019-03-05 | Palantir Technologies Inc. | Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data |
US11501369B2 (en) | 2015-07-30 | 2022-11-15 | Palantir Technologies Inc. | Systems and user interfaces for holistic, data-driven investigation of bad actor behavior based on clustering and scoring of related data |
US10484407B2 (en) | 2015-08-06 | 2019-11-19 | Palantir Technologies Inc. | Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications |
US9635046B2 (en) | 2015-08-06 | 2017-04-25 | Palantir Technologies Inc. | Systems, methods, user interfaces, and computer-readable media for investigating potential malicious communications |
US10489391B1 (en) | 2015-08-17 | 2019-11-26 | Palantir Technologies Inc. | Systems and methods for grouping and enriching data items accessed from one or more databases for presentation in a user interface |
US11048706B2 (en) | 2015-08-28 | 2021-06-29 | Palantir Technologies Inc. | Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces |
US9898509B2 (en) | 2015-08-28 | 2018-02-20 | Palantir Technologies Inc. | Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces |
US10346410B2 (en) | 2015-08-28 | 2019-07-09 | Palantir Technologies Inc. | Malicious activity detection system capable of efficiently processing data accessed from databases and generating alerts for display in interactive user interfaces |
US10572487B1 (en) | 2015-10-30 | 2020-02-25 | Palantir Technologies Inc. | Periodic database search manager for multiple data sources |
US10318630B1 (en) | 2016-11-21 | 2019-06-11 | Palantir Technologies Inc. | Analysis of large bodies of textual data |
US11681282B2 (en) | 2016-12-20 | 2023-06-20 | Palantir Technologies Inc. | Systems and methods for determining relationships between defects |
US10620618B2 (en) | 2016-12-20 | 2020-04-14 | Palantir Technologies Inc. | Systems and methods for determining relationships between defects |
US10325224B1 (en) | 2017-03-23 | 2019-06-18 | Palantir Technologies Inc. | Systems and methods for selecting machine learning training data |
US11481410B1 (en) | 2017-03-30 | 2022-10-25 | Palantir Technologies Inc. | Framework for exposing network activities |
US10606866B1 (en) | 2017-03-30 | 2020-03-31 | Palantir Technologies Inc. | Framework for exposing network activities |
US11947569B1 (en) | 2017-03-30 | 2024-04-02 | Palantir Technologies Inc. | Framework for exposing network activities |
US11210350B2 (en) | 2017-05-02 | 2021-12-28 | Palantir Technologies Inc. | Automated assistance for generating relevant and valuable search results for an entity of interest |
US11714869B2 (en) | 2017-05-02 | 2023-08-01 | Palantir Technologies Inc. | Automated assistance for generating relevant and valuable search results for an entity of interest |
US10235461B2 (en) | 2017-05-02 | 2019-03-19 | Palantir Technologies Inc. | Automated assistance for generating relevant and valuable search results for an entity of interest |
US10482382B2 (en) | 2017-05-09 | 2019-11-19 | Palantir Technologies Inc. | Systems and methods for reducing manufacturing failure rates |
US11537903B2 (en) | 2017-05-09 | 2022-12-27 | Palantir Technologies Inc. | Systems and methods for reducing manufacturing failure rates |
US11954607B2 (en) | 2017-05-09 | 2024-04-09 | Palantir Technologies Inc. | Systems and methods for reducing manufacturing failure rates |
US10838987B1 (en) | 2017-12-20 | 2020-11-17 | Palantir Technologies Inc. | Adaptive and transparent entity screening |
US11119630B1 (en) | 2018-06-19 | 2021-09-14 | Palantir Technologies Inc. | Artificial intelligence assisted evaluations and user interface for same |
US11847668B2 (en) * | 2018-11-16 | 2023-12-19 | Bread Financial Payments, Inc. | Automatically aggregating, evaluating, and providing a contextually relevant offer |
US20220027934A1 (en) * | 2018-11-16 | 2022-01-27 | Comenity Llc | Automatically aggregating, evaluating, and providing a contextually relevant offer |
US11164206B2 (en) * | 2018-11-16 | 2021-11-02 | Comenity Llc | Automatically aggregating, evaluating, and providing a contextually relevant offer |
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